Systematic analysis of proteins is essential in understanding human diseases and their clinical treatments. To achieve the rapid and unambiguous identification of marker or target proteins, a new procedure termed "inverse labeling" is proposed. With this procedure, to evaluate protein expression of a diseased or a drug-treated sample in comparison with a control sample, two converse labeling experiments are performed in parallel. The perturbed sample (by disease or by drug treatment) is labeled in one experiment, whereas the control is labeled in the second experiment. When mixed and analyzed with its unlabeled counterpart for differential comparison using mass spectrometry, a characteristic inverse labeling pattern of mass shift will be observed between the two parallel analyses for proteins that are differentially expressed. In this study, protein labeling is achieved through 18O incorporation into peptides by proteolysis performed in [18O]water. Once the peptides are identified with the characteristic inverse labeling pattern of 18O/16O ion intensity shift, MS data of peptide fingerprints or peptide sequence information can be used to search a protein database for protein identification. The methodology has been applied successfully to two model systems in this study. It permits quick focus on the signals of differentially expressed proteins. It eliminates the detection ambiguities caused by the dynamic range of detection on proteins of extreme changes in expression. It enables the detection of protein modifications responding to perturbation. This strategy can also be extended to other protein-labeling methods, such as chemical or metabolic labeling, to realize the same benefits.

No abstract

The bile-pigment chromophores of C-phycoerythrin (phycoerythrobilin) and C-phycocyanin (phycocyanobilin) were cleaved from their respective proteins with boiling methanol or butan-1-ol. They were purified as dicarboxylic acids by preparative reverse-phase liquid chromatography. Each pigment existed in two principal forms, which were characterized by using 20 pmol samples by proton-transfer chemical-ionization mass spectroscopy. These two principal forms were isomeric species, and all had protonated parent molecular ions with m/e 587, corresponding to a molecular weight of 586.

The inverse labeling/mass spectrometry strategy has been applied to protein metabolic (15)N labeling for gel-free proteomics to achieve the rapid identification of protein markers/targets. Inverse labeling involves culturing both the perturbed (by disease or by a drug treatment) and control samples each in two separate pools of normal and (15)N-enriched culture media such that four pools are produced as opposed to two in a conventional labeling approach. The inverse labeling is then achieved by combining the normal (14)N-control with the (15)N-perturbed sample, and the (15)N-control with the (14)N-perturbed sample. Both mixtures are then proteolyzed and analyzed by mass spectrometry (coupled with on-line or off-line separation). Inverse labeling overcomes difficulties associated with protein metabolic labeling with regard to isotopic peak correlation and data interpretation in the single-experiment approach (due to the non-predictable/variable mass difference). When two data sets from inverse labeling are compared, proteins of differential expression are readily recognized by a characteristic inverse labeling pattern or apparent qualitative mass shifts between the two inverse labeling analyses. MS/MS fragmentation data provide further confirmation and are subsequently used to search protein databases for protein identification. The methodology has been applied successfully to two model systems in this study. Utilizing the inverse labeling strategy, one can use any mass spectrometer of standard unit resolution, and acquire only the minimum, essential data to achieve the rapid and unambiguous identification of differentially expressed protein markers/targets. The strategy permits quick focus on the signals of differentially expressed proteins. It eliminates the detection ambiguities caused by the dynamic range of detection. Finally, inverse labeling enables the detection of covalent changes of proteins responding to a perturbation that one might fail to distinguish with a conventional labeling experiment.

No abstract